ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
Deep Learning in a Computational Model for Conceptual Shifts in a Co-Creative Design System
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
This paper presents a computational model for conceptual shifts, based on a novelty metric applied to a vector representation generated through deep learning. This model is integrated into a co-creative design system, which enables a partnership between an AI agent and a human designer interacting through a sketching canvas. The AI agent responds to the human designer's sketch with a new sketch that is a conceptual shift: intentionally varying the visual and conceptual similarity with increasingly more novelty. The paper presents the results of a user study showing that increasing novelty in the AI contribution is associated with higher creative outcomes, whereas low novelty leads to less creative outcomes.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Proposes Interaction-Centered Intelligence as a framework where intelligence emerges from interaction dynamics rather than internal agent computation.
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